Similar to most sub-fields in computer vision, advances in deep learning, which is inspired by the nervous system [1]}, have led to significant face PAD improvements on benchmark datasets using convolutional neural network-based (CNN) end-to-end representation learning and classification [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. Following the standard pipeline of supervised learning for deep learning, a large, labeled training dataset, which consists of known attacks and bona-fide data points, is collected, and then used to train a deep network with a suitable architecture [11]}, [1]}, [13]}.
